A Simple Scoring System to Differentiate between Relapse and Re-Infection in Patients with Recurrent Melioidosis

Melioidosis is a serious infectious disease caused by the Gram-negative bacterium, Burkholderia pseudomallei. This organism is present in the environment in areas where melioidosis is endemic (most notably East Asia and Northern Australia), and infection is acquired following bacterial inoculation or inhalation. Despite prolonged oral eradicative treatment, recurrent melioidosis occurs in approximately 10% of survivors of acute melioidosis. Recurrent melioidosis can be caused by relapse (failure of initial eradicative treatment) or re-infection with a new infection. The aim of this study was to develop a simple scoring system to distinguish between re-infection and relapse, since this has implications for antimicrobial treatment of the recurrent episode, but telling the two apart normally requires bacterial genotyping. A prospective study of melioidosis patients in NE Thailand conducted between 1986 and 2005 identified 141 patients with recurrent melioidosis. Of these, 92 patients had relapse and 49 patients had re-infection as confirmed by genotyping techniques. We found that relapse was associated with previous inadequate treatment and shorter time to clinical features of recurrence, while re-infection was associated with renal insufficiency and presentation during the rainy season. A simple scoring index to help distinguish between relapse and re-infection was developed to provide important bedside information where rapid bacterial genotyping is unavailable. Guidelines are provided on how this scoring system could be implemented

Structure of Tabernoxidine, a novel oxindole alkaloid from Tabernaemontana heyneana wall

Tabernoxidine, an oxindole alkaloid isolated from the leaves of Tabernaemontana heyneana Wall, is shown to have the structure (I) on the basis of spectral data and X-ray crystollagraphy

Novel prediction score including pre- and intraoperative parameters best predicts acute kidney injury after liver surgery

BACKGROUND: A recently published score predicts the occurrence of acute kidney injury (AKI) after liver resection based on preoperative parameters (chronic renal failure, cardiovascular disease, diabetes, and alanine-aminotransferase levels). By inclusion of additional intraoperative parameters we aimed to develop a new prediction model. METHODS: A series of 549 consecutive patients were enrolled. The preoperative score and intraoperative parameters (blood transfusion, hepaticojejunostomy, oliguria, cirrhosis, diuretics, colloids, and catecholamine) were included in a multivariate logistic regression model. We added the strongest predictors that improved prediction of AKI compared to the existing score. An internal validation by fivefold cross validation was performed, followed by a decision curve analysis to evaluate unnecessary special care unit admissions. RESULTS: Blood transfusions, hepaticojejunostomy, and oliguria were the strongest intraoperative predictors of AKI after liver resection. The new score ranges from 0 to 64 points predicting postoperative AKI with a probability of 3.5-95 %. Calibration was good in both models (15 % predicted risk vs. 15 % observed risk). The fivefold cross-validation indicated good accuracy of the new model (AUC 0.79 (95 % CI 0.73-0.84)). Discrimination was substantially higher in the new model (AUCnew 0.81 (95 % CI 0.76-0.86) versus AUCpreoperative 0.60 (95 % CI 0.52-0.69), p < 0.001). The new score could reduce up to 84 unnecessary special care unit admissions per 100 patients depending on the decision threshold. CONCLUSIONS: By combining three intraoperative parameters with the existing preoperative risk score, a new prediction model was developed that more accurately predicts postoperative AKI. It may reduce unnecessary admissions to the special care unit and support management of patients at higher risk